An effective Power Quality classifier using Wavelet Transform and Support Vector Machines

作者：De Yong D; Bhowmik S; Magnago F

来源：Expert Systems with Applications, 2015, 42(15-16): 6075-6081.

DOI：10.1016/j.eswa.2015.04.002

• Power Quality
• Wavelet Transform
• Support Vector Machine
• Complex disturbance detection and classification

In this paper we propose a method based on a combination of binary classifiers which are optimized for those special cases where the real signals contain a multitude of events within the analyzed temporal These type of events are known as complex events. The proposed Power Quality (PQ) classifier is based on Wavelet Transforms (WT) and Support Vector Machines (SVM). The method uses a One vs. One multiclass SVM. We propose a novel method which is simple, easy to train, and can be implemented with low computational cost. The proposed algorithm consists of a set of simple binary SVM classifiers. Each SVM node is trained separately allowing them to be parallelized. The training stage is performed using single events, however due to the structure of the SVM methodology selected, it allows the system to detect complex events. Tests and training were performed using real complex signals and the results show the proposed methodology to be highly efficient.

• 出版日期2015-9